A distributed, cloud-ready, digital content processing and transformation platform and a specific use case

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Big Data Processing in the Cloud: a Hydra/Sufia Experience

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Video transcoding in a distributed cloud computing environment

Video transcoding refers to the process of converting a digital video from one format into another format. It is a compute-intensive operation. Therefore, transcoding of a large number of simultaneous video streams requires a large amount of computing resources. Moreover, to handle di erent load conditions in a cost-e cient manner, the video transcoding service should be dynamically scalable. Infrastructure as a Service Clouds currently offer computing resources, such as virtual machines, under the pay-per-use business model. Thus the IaaS Clouds can be leveraged to provide a coste cient, dynamically scalable video transcoding service. To use computing resources e ciently in a cloud computing environment, cost-e cient virtual machine provisioning is required to avoid overutilization and under-utilization of virtual machines. This thesis presents proactive virtual machine resource allocation and de-allocation algorithms for video transcoding in cloud computing. Since users' requests for videos may change at di erent times, a check is required to see if the current computing resources are adequate for the video requests. Therefore, the work on admission control is also provided. In addition to admission control, temporal resolution reduction is used to avoid jitters in a video. Furthermore, in a cloud computing environment such as Amazon EC2, the computing resources are more expensive as compared with the storage resources. Therefore, to avoid repetition of transcoding operations, a transcoded video needs to be stored for a certain time. To store all videos for the same amount of time is also not cost-e cient because popular transcoded videos have high access rate while unpopular transcoded videos are rarely accessed. This thesis provides a cost-e cient computation and storage trade-o strategy, which stores videos in the video repository as long as it is cost-e cient to store them. This thesis also proposes video segmentation strategies for bit rate reduction and spatial resolution reduction video transcoding. The evaluation of proposed strategies is performed using a message passing interface based video transcoder, which uses a coarse-grain parallel processing approach where video is segmented at group of pictures level.

Improving the competitiveness of electrolytic Zinc process by chemical reaction engineering approach

This doctoral thesis describes the development work performed on the leachand purification sections in the electrolytic zinc plant in Kokkola to increase the efficiency in these two stages, and thus the competitiveness of the plant. Since metallic zinc is a typical bulk product, the improvement of the competitiveness of a plant was mostly an issue of decreasing unit costs. The problems in the leaching were low recovery of valuable metals from raw materials, and that the available technology offered complicated and expensive processes to overcome this problem. In the purification, the main problem was consumption of zinc powder - up to four to six times the stoichiometric demand. This reduced the capacity of the plant as this zinc is re-circulated through the electrolysis, which is the absolute bottleneck in a zinc plant. Low selectivity gave low-grade and low-value precipitates for further processing to metallic copper, cadmium, cobalt and nickel. Knowledge of the underlying chemistry was poor and process interruptions causing losses of zinc production were frequent. Studies on leaching comprised the kinetics of ferrite leaching and jarosite precipitation, as well as the stability of jarosite in acidic plant solutions. A breakthrough came with the finding that jarosite could precipitate under conditions where ferrite would leach satisfactorily. Based on this discovery, a one-step process for the treatment of ferrite was developed. In the plant, the new process almost doubled the recovery of zinc from ferrite in the same equipment as the two-step jarosite process was operated in at that time. In a later expansion of the plant, investment savings were substantial compared to other technologies available. In the solution purification, the key finding was that Co, Ni, and Cu formed specific arsenides in the “hot arsenic zinc dust” step. This was utilized for the development of a three-step purification stage based on fluidized bed technology in all three steps, i.e. removal of Cu, Co and Cd. Both precipitation rates and selectivity increased, which strongly decreased the zinc powder consumption through a substantially suppressed hydrogen gas evolution. Better selectivity improved the value of the precipitates: cadmium, which caused environmental problems in the copper smelter, was reduced from 1-3% reported normally down to 0.05 %, and a cobalt cake with 15 % Co was easily produced in laboratory experiments in the cobalt removal. The zinc powder consumption in the plant for a solution containing Cu, Co, Ni and Cd (1000, 25, 30 and 350 mg/l, respectively), was around 1.8 g/l; i.e. only 1.4 times the stoichiometric demand – or, about 60% saving in powder consumption. Two processes for direct leaching of the concentrate under atmospheric conditions were developed, one of which was implemented in the Kokkola zinc plant. Compared to the existing pressure leach technology, savings were obtained mostly in investment. The scientific basis for the most important processes and process improvements is given in the doctoral thesis. This includes mathematical modeling and thermodynamic evaluation of experimental results and hypotheses developed. Five of the processes developed in this research and development program were implemented in the plant and are still operated. Even though these processes were developed with the focus on the plant in Kokkola, they can also be implemented at low cost in most of the zinc plants globally, and have thus a great significance in the development of the electrolytic zinc process in general.

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.